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STAT40510

Academic Year 2024/2025

Applied Statistical Modelling (STAT40510)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Patrick Murphy
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module aims to develop students real-world skills in data analysis and modeling.

Students will perform independent analysis of real datasets using the techniques of applied statistics and mathematical modeling learned from previous study.

This module will develop students ability to work in a group, choose and apply appropriate analysis techniques, and present their results in a clear and rigorous fashion.

About this Module

Learning Outcomes:

Students will develop skills in:

Design of data analysis plans to answer research questions
Exploratory and descriptive analysis of data
Statistical and mathematical data modeling
Presentation of research results
Academic writing, presentation, and producing reports of modeling results

Indicative Module Content:

Application of mathematical and statistical modelling techniques to real data sets including:
Linear Modelling, Generalised Linear Modelling, Time Series, Survival Analysis, Multivariate Analysis

Application of applied mathematical modeling techniques including:
Epidemiological models, Pharmacokinetic models

Research methods, report writing and presentation skills

Student Effort Hours:
Student Effort Type Hours
Lectures

2
Specified Learning Activities

50
Autonomous Student Learning

50
Total

102

Approaches to Teaching and Learning:
This is a project-based module, which focuses on application of analysis techniques to data in group projects.

The module includes some lectures to revise or introduce relevant research methods, optional workshops covering certain analysis techniques, and presentation sessions where students will show the results of their analysis and receive feedback from lecturers.

Students will be graded on their group presentations and research reports, with emphasis on both validity of analysis and presentation and communication of results. 

Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have a thorough understanding of basic statistical inference including hypothesis testing and regression models. Students should also have a basic knowledge of Ordinary Differential Equations.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Group projects Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
100
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Written feedback will be provided to each group by email.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21 Mon 14:00 - 15:50
Spring Lecture Offering 1 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 14:00 - 15:50
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